An autonomous vehicle is one which is capable of sensing its environment and navigating without the use of human input. It is envisioned that such vehicles will be capable of transitioning from an autonomous driving mode and a manual driving mode, in which a driver manually operates the vehicle. It is further envisioned that such autonomous driving may only be allowed on preapproved or certified roads or zones. Thus, a vehicle's initial driving segment will likely require the human driver to control the vehicle and later transition to an autonomous driving mode. While in an autonomous driving mode, a driver of a vehicle may engage in activities which may not be possible while the vehicle is in a manual driving mode. Examples of such activities are sleeping, working or using multimedia applications. Final segment may be initiated by the driver taking back control of the vehicle to depart the certified road and driving manually until destination is reached.
Self-driving vehicles need highly accurate positioning information to navigate safely along a pre-defined route. To obtain the needed accuracy, the vehicle is equipped with both GPS, camera, radar and lidar sensors, combined with an inertial measurement unit (IMU) for estimating the ego vehicle state in terms of speed, acceleration and rotation. Affordable IMUs typically suffer from bias and scale errors which degrades the positioning accuracy.
Suppliers of IMUs use model based algorithms to estimate and compensate for the bias and scale errors. In addition, some suppliers use information from Doppler-shift radar and cameras with respect to stationary objects to further improve the accuracy of vehicle speed and yaw rate estimation. This compensation is usually sufficient for most vehicle dynamics and active safety functions. However, the requirements for positioning an ego vehicle state estimation for self-driving cars are much higher as compared to existing functionality. Particularly, these requirements are hard to fulfil when driving in environments with few stationary objects or with degraded GPS information, such as in tunnels and in areas with high buildings or objects beside the road.
Dead reckoning is today implemented in some high-end automotive navigation systems in order to overcome the limitations of GPS/GNSS technology alone. Satellite microwave signals are unavailable in parking garages and tunnels, and often severely degraded in urban canyons and near trees due to blocked lines of sight to the satellites or multipath propagation. In a dead-reckoning navigation system, the car is equipped with sensors that know the wheel diameter and record wheel rotations and steering direction. These sensors are often already present in cars for other purposes, such as anti-lock braking system, and electronic stability control, and can be read by the navigation system from the controller-area network bus. The navigation system then uses a Kalman filter to integrate the always-available sensor data with the accurate but occasionally unavailable position information from the satellite data into a combined position fix (see, e.g., http://en.wikipedia.org/wiki/Dead_reckoning).
WO14130854 A1 discloses various techniques for use within a vision-aided inertial navigation system (VINS). A VINS comprises an image source to produce image data comprising a plurality of images, and an inertial measurement unit (IMU) to produce IMU data indicative of a motion of the vision-aided inertial navigation system while producing the image data, wherein the image data captures features of an external calibration target that is not aligned with gravity. The VINS further includes a processing unit comprising an estimator that processes the IMU data and the image data to compute calibration parameters for the VINS concurrently with computation of a roll and pitch of the calibration target, wherein the calibration parameters define relative positions and orientations of the IMU and the image source of the vision-aided inertial navigation system.
US2012022780 AA discloses apparatus and methods for calibrating dynamic parameters of a vehicle navigation system. One method may include determining whether reference position data of a vehicle is available, and measuring composite accelerations of the vehicle. The method may further include generating distance and turn angle data based upon a wheel speed sensors data, computing distance and turn angle errors based upon the independent position data, and associating the distance and turn angle errors with composite accelerations. A second method presented includes calibrating an inertial navigation sensor within a vehicle navigation system. The second method may include determining reference position data and Inertial Navigation System (INS) data, aligning an IMU with the vehicle, and aligning the IMU with an Earth fixed coordinate system. The second method may further include computing the vehicle alignment with respect to a horizontal plane, and determining calibration parameters for distance sensors associated with the vehicle.